Abstract
AbstractBy utilizing only depth information, the paper introduces a novel two-stage planning approach that enhances computational efficiency and planning performances for memoryless local planners. First, a depth-based sampling technique is proposed to identify and eliminate a specific type of in-collision trajectories among sampled candidates. Specifically, all trajectories that have obscured endpoints are found through querying the depth values and will then be excluded from the sampled set, which can significantly reduce the computational workload required in collision checking. Subsequently, we apply a tailored local planning algorithm that employs a direction cost function and a depth-based steering mechanism to prevent the robot from being trapped in local minima. Our planning algorithm is theoretically proven to be complete in convex obstacle scenarios. To validate the effectiveness of our DEpth-based both Sampling and Steering (DESS) approaches, we conducted experiments in simulated environments where a quadrotor flew through cluttered regions with multiple various-sized obstacles. The experimental results show that DESS significantly reduces computation time in local planning compared to the uniform sampling method, resulting in the planned trajectory with a lower minimized cost. More importantly, our success rates for navigation to different destinations in testing scenarios are improved considerably compared to the fixed-yawing approach.
Funder
Federation University Australia
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Artificial Intelligence,Industrial and Manufacturing Engineering,Mechanical Engineering,Control and Systems Engineering,Software
Reference37 articles.
1. Popovic, M., Thomas, F., Papatheodorou, S., Funk, N., Vidal-Calleja, T., Leutenegger, S.: Volumetric occupancy mapping with probabilistic depth completion for robotic navigation. IEEE Robot. Autom. Lett. 6, 5072–5079 (2021). https://doi.org/10.1109/LRA.2021.3070308
2. Dey, R.: Monodepth-vslam: a visual ekf-slam using optical flow and monocular depth estimation. (2021). http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666226301079. Accessed 1 June 2023
3. Quan, L., Han, L., Zhou, B., Shen, S., Gao, F.: Survey of uav motion planning. IET Cyber-Syst. Robot. 2, 14–21 (2020). https://doi.org/10.1049/IET-CSR.2020.0004
4. Florence, P., Carter, J., Tedrake, R.: Integrated perception and control at high speed: Evaluating collision avoidance maneuvers without maps. Springer Proceed. Adv. Robot. 13, 304–319 (2020). https://doi.org/10.1007/978-3-030-43089-4_20
5. Lopez, B.T., How, J.P.: Aggressive 3-d collision avoidance for high-speed navigation. Proceedings - IEEE International Conference on Robotics and Automation, 5759–5765 (2017) https://doi.org/10.1109/ICRA.2017.7989677
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献